2022 Annual Meeting
(582a) Crystal Engineering of a Zeolite Using Machine Learning
Authors
Xinyu Li - Presenter, University of Minnesota
Nikolas Evangelou - Presenter, Johns Hopkins University
Noah J. Wichrowski - Presenter, Johns Hopkins University
He Han, Dalian University of Technology
Peng Lu, Johns Hopkins University
Wenqian Xu, Argonne National Laboratory
Son-Jong Hwang, Caltech
Wenyang Zhao, University of Minnesota
Chunshan Song, The Chinese University of Hong Kong
Xinwen Guo, Dalian University of Technology
Aditya Bhan, University of Minnesota
Ioannis G. Kevrekidis, Princeton University
Michael Tsapatsis, Johns Hopkins University
It is shown that machine learning (ML) algorithms can capture the effect of crystallization inputs on key microstructural characteristics (outputs) of faujasite (FAU), a widely used zeolite catalyst and adsorbent. This work focuses primarily on using the technique known as geometric harmonics to learn input-output relationships of interest, but we also provide a brief comparison with neural networks and Gaussian process regression, as alternative approaches. Through ML, synthesis conditions were identified to enhance the Si/Al ratio of (FAU) zeolite prepared via direct template-free synthesis to the hitherto highest level (i.e., Si/Al = 3.5). Our analysis of the ML algorithmsâ results offers the insight that reduced Na2O content is a key parameter to achieve the enhanced Si/Al ratio. An acid catalyst prepared by partial ion exchange of the new high-Si/Al-ratio FAU (Si/Al=3.5) exhibits improved proton reactivity in propane cracking and dehydrogenation compared to the catalyst prepared from the previously reported highest Si/Al ratio (Si/Al=2.8).